Here we introduce a logical-qubit representation of OAM propagation in which modes evolve as qubits through noisy quantum channels. In this representation, turbulence manifests as leakage from the logical OAM subspace into turbulence-induced leakage components, providing a unified description of degradation in both single-photon and coherent-beam regimes. 

We establish an exact equivalence in distribution between D-dimensional random mixed states induced by partial traces over K-dimensional environments and the Mai-Alquier distribution, a mixture of K independent Haar-random pure states weighted by a symmetric Dirichlet distribu tion. 

We demonstrate that a reinforcement‑learning agent based on a deep Q learning network can autonomously discover high‑performance pulse sequences using only system parameters and a reward function. The approach is fully general for superconducting qubit platforms, requires no ansatz, and operates at nanosecond resolution compatible with hardware constraints.

 We derive balanced two-port estimators whose denominators are analyzer independent (completeness / no signaling), together with Fisher information (FI) and Cram'er--Rao bounds (CRBs) that establish an equivalence to time division under labeled randomization. 

We investigate the impact of quantum noise channels across the encoding and measurement stages within a hybrid quantum-classical distributed system. We also introduce noise into selected gate operations and measure ments to demonstrate its distinct effects on the quantum circuit. 

We build a general quantum state tomography framework that makes use of machine learning techniques to reconstruct quantum states from a given set of coincidence measurements. For a wide range of pure and mixed input states we demonstrate via simulations that our method produces functionally equivalent reconstructed states to that of traditional methods with the added benefit that expensive computations are front-loaded with our system. 

We develop a state-of-the-art generative neural network (GNN) and convolutional neural network (CNN) system in combination, and demonstrate its efficacy in simulated and experimental communications settings. Experimentally, the GNN system corrects for distortion and reduces detector noise, resulting in nearly identical-to-desired mode profiles at the receiver, requiring no feedback or adaptive optics. 

Here we design an optical communication scheme that uses balanced homodyne detection in combination with an unsupervised generative machine learning and convolutional neural network (CNN) system, and demonstrate its efficacy in a realistic simulated coherent quadrature phase shift keyed (QPSK) communications system. 

We design an optical feedback network making use of machine learning techniques and demonstrate via simulations its ability to correct for the effects of turbulent propagation on optical modes. This artificial neural network scheme only relies on measuring the intensity profile of the distorted modes, making the approach simple and robust. 

In this article, the self‐learning and self‐evolving features of artificial neural networks are exploited to correct the complex spatial profile of distorted Laguerre–Gaussian modes at the single‐photon level. Furthermore, the potential of this technique is used to improve the channel capacity of an optical communication protocol that relies on structured single photons. 

In this work we demonstrate the efficacy of neural networks in the characterization of dispersive media. We also develop a neural network to make predictions for input probe pulses which propagate through a nonlinear dispersive medium, which may be applied to predicting optimal pulse shapes for a desired output. 

we demonstrate the ability of deep neural networks to classify numerically generated, noisy Laguerre–Gauss modes of up to 100 quanta of orbital angular momentum with near-unity fidelity.  The scheme relies only on the intensity profile of the detected modes, allowing for considerable simplification of current measurement schemes required to sort the states containing increasing degrees of orbital angular momentum.